AI Revolutionizes Fintech: Reshaping Credit Assessment and Lending Practices by 2025

Fintech Predictions for 2025: What's Next?: By Katherine Chan

2025 is a defining moment for fintech. Indeed, the sector is innovating wildly, driven by embedded finance, alternative lending models, and AI; it's also being reshaped by new regulations and shifts in funding. The last year has been a real test of the resilience of financial services as investments start to tighten, economic uncertainty increases, and regulatory frameworks continue changing. Those that move fastest will shape the next stage of growth in fintech. Fintech investment is getting a reset in the UK. While the total deal activity saw a drop of 61% in 2024, the average deal value reached an all-time high at $21.8 million, indicating that fewer but more qualitative investments are being made. This would indeed be a sign that investors are moving toward profitability instead of rapid expansion-a trend that will redefine how fintech companies raise money. Meanwhile, the Bank of England's Prudential Regulation Authority is relaxing key rules, including removing a cap on bankers' bonuses and dialing back some reporting requirements. That is part of a broader effort to boost economic competitiveness by giving financial firms incentive to take smart risks. But economic change is not constrained to the UK. Global financial markets are also being stretched to their limits by an escalation of trade tensions and increasing geopolitical uncertainty. The US has raised tariffs on imports from Canada, Mexico, and China, raising concerns about a long trade war and causing volatility in markets. Meanwhile, global risk perception is at an all-time high, with the Doomsday Clock at 89 seconds to midnight amid growing apprehensions about economic uncertainty, geopolitical tension, and AI-driven risks. These forces are global in nature and will shape the path that investors, regulators, and financial firms take toward innovation in 2025. Having operated in both banking and fintech, I've seen firsthand how this movement in regulation, risk appetite, and investment trend is directly affecting the bottom line. Today, the digital-first company and fintech lender have to operate against a landscape where funding strategies are changing, compliance pressures are mounting, and AI changes the game in financial decision-making. In this article, I explore five key trends that will define fintech in 2025, and what they mean for businesses who want to get ahead. AI is really changing the game in credit risk assessment, shifting away from static backward-looking credit models to real-time financial insights that paint a far more accurate picture of borrower risk. Traditional lending methods rely on historic data from financial statements, credit scores, and collateral, and often shut out high-growth startups and SMEs with non-traditional revenue streams. A new generation of open finance frameworks is helping drive fintech lenders' usage of alternative data sources for dynamic assessments of financial health. According to HSBC's Access to Finance report, 2024, the increased access to SME credit data due to the SBEE Act will eventually provide an opportunity for challenger banks and alternative finance providers to further extend their lending scope. AI-driven models accelerate this trend since it equips lenders to assess businesses based on real-time transaction patterns and cash flow, rather than outdated credit scores. Beyond increasing access to capital, AI is also enhancing fairness in lending decisions. According to HSBC's report, one of the main obstacles to SME lending has been the fact that credit data has traditionally been controlled by a few major providers, making it hard for new entrants to accurately assess borrower risk. By expanding credit data availability, AI-driven credit risk models can provide greater financial inclusion, ensuring viable businesses are not unfairly excluded from funding opportunities. While traditional credit models only gauge risk by using past financial statements and credit history-a narrow view of the business's financial health-AI-driven models look at real-time revenue patterns, customer transactions, and industry trends. This helps lenders make much quicker and more precise funding decisions. This means: Deloitte's research underlines how AI-powered credit models predict default risks more precisely, enabling lenders to approve credit for viable businesses that would otherwise have been rejected by traditional models. The fairness of credit decision-making is the most widely pressed criticism of conventional lending, mostly resulting in overly restricted access to capital. Artificial intelligence allows alleviating such biases by: Beyond better risk assessment, AI is changing how credit is structured and delivered. By analysing business performance in real time, lenders can offer: With the ability of AI to constantly monitor financial health, this allows lenders to refine risk assessment over time, making sure funding remains with the performance of the business. This is a big departure from the traditional model of lending, which locks borrowers into rigid structures of repayment based on outdated projections. In 2025, one of the defining features of fintech has been embedded finance: taking financial products and embedding them directly into platforms with which businesses and consumers are already engaged. In so doing, this is removing friction from financial services and making access to lending, payments, and banking frictionless in ways never before possible. Traditional financial services usually require businesses to apply for loans, open an account, or integrate third-party payment systems manually. Embedded finance eliminates these steps by offering financial tools within existing platforms, including: Meanwhile, the fast growth of Banking-as-a-Service and API-driven platforms accelerated this growth in the adoption of embedded finance. Instead of taking traditional banks as the sole provider of financial services, fintech companies partner with software platforms for: Businesses no longer need to navigate separate applications for financial services, reducing administrative work and improving cash flow access. For SMEs, embedded finance offers more than just convenience -- it provides a strategic advantage. Businesses that leverage embedded financial services can: This will give a whole new shape to the face of Fintech while traditional banks and independent lenders alike will be pitched against platforms which directly embed their financial services. Companies that embedded finance will make up for more liquidity and better financial flexibility. The use of fintech lenders is becoming more flexible in terms of avoiding fixed-term loans and rigid structures of repayment by offering funding solutions that work around the financial cycle of a business. While BNPL has already reshaped consumer finance, in 2025, similar models are gaining traction in B2B lending, revenue-based financing, and on-demand credit solutions. BNPL has typically been associated with consumer purchases: the ability for buyers to divide a payment into manageable instalments. However, BNPL fintechs now move into business areas and expand on offering delay payments to small- and medium-sized enterprises, which include inventories, software, and operation expenses. This is especially true for companies that face seasonal working capital oscillations or large upfronts: Deloitte's report on the future of payments highlights BNPL's expansion into business lending, with fintechs partnering with wholesalers, SaaS providers, and supply chain platforms to offer payment flexibility. Revenue-based financing instead permits repayment of a share of revenues against fixed loan repayments, thus aligning funding with actual income instead of pre-determined instalments. This model works to the benefit of: The fintech lenders apply dynamic risk assessments with AI-driven revenue analytics to make the repayment schedule flexible. This approach replaces the one-size-fits-all lending model that often strained SMEs when sales are low. Apart from BNPL and revenue-based financing, on-demand credit lines are one of the most in-demand options for companies in need of access to short-term capital. This form of credit solution allows a company to: It works particularly well for businesses that have very inconsistent income streams, or those investing heavily in marketing, inventory, or scaling operations. Sustainability is no longer a nice-to-have in financial services. In 2025, ESG considerations are playing an increasingly important role in how fintechs, investors, and lenders conduct their operations. Regulators, consumers, and businesses alike want financial products that align with their sustainability objectives, meaning the fintech sector has to embed ESG into its lending and investment decisions. ESG-driven financing is gathering steam, with lenders increasingly considering sustainability metrics in credit decisions. Therefore, companies with the best ESG practices get to enjoy preferential loan terms, sustainability-linked credit lines, and green financing. Some of the key happenings taking place include: According to Deloitte's Financial Services Outlook, financial institutions are incorporating ESG factors into their risk models, so that access to funds is higher in businesses with good sustainability initiatives. Regulatory Pressure on ESG Compliance Governments and financial regulators are putting on pressure for increased transparency of ESG reporting, which requires businesses and lenders to show their environmental and social impact. This changes the way in which fintechs build their products, with a view to ensuring that financing models fall within regulatory frameworks. Different from traditional financial institutions, the ability of fintech companies to bake ESG into new, digital-first lending models includes the following: Only fintech lenders with incorporated sustainability-driven models will emerge successful in both attracting investors and businesses that pursue ethical financing alternatives. Regulators have started to take a closer look at fintech lending, putting stricter compliance rules on AI-driven credit assessments, open banking, and consumer protection. With alternative finance continuing to rise, going forward, businesses must be prepared for added scrutiny and regulatory change-forced innovation in the way it operates. AI is increasingly central to credit risk assessment, but regulators are concerned about bias, transparency, and fairness in automated decision-making. In response, financial authorities are pushing for: Open banking is still expanding, but new regulations are starting to define how financial data is shared between fintechs and incumbent banks. These new regulations aim to do the following: give more protection to consumer data, with more stringent security requirements for open banking APIs; make financial records more transparent, ensuring lenders use customer data responsibly; standardize data-sharing protocols, enabling fintechs to integrate with banking systems more securely. Proactive adaptation to these new regulations, however, can be the key strategic competitive advantage for fintech lenders. Companies that address compliance, transparency, and ethics in their lending will: In the ever-changing face of fintech, AI, embedded finance, and flexible funding models are rewriting the rulebook on how businesses access capital. As lenders continue moving away from rigid credit models, technology is making quicker, fairer, more tailored financial solutions for SMEs possible. Simultaneously, ESG-linked financing and regulatory oversight force the industry into lending practices that are more transparent, responsible, and sustainable. Key Takeaways:

Does AI Innovation Spell the End for Credit Brokers?: By Taras Boyko

Few tech innovations have been as transformative as generative artificial intelligence. Since ChatGPT's launch in November 2022, the AI landscape has evolved dramatically, challenging long-held norms and reshaping entire industries. The source of the latest shake-up has been DeepSeek, whose sudden emergence heaped pressure on industry leaders like OpenAI, Google, and Meta. The Chinese AI lab's debut also sent shockwaves through the wider tech sector, triggering a market sell-off that wiped over $1 trillion from U.S. and European technology stocks in a single day, and saw Nvidia lose $600 billion in market capitalisation -- the steepest one-day decline by that measure for any company in U.S. stock market history. For credit brokers and the broader financial industry, these developments signal both a challenge and an opportunity. Heightened competition in the AI space is likely to drive down costs and foster more accessible AI solutions, empowering businesses to streamline processes, enhance risk assessments, and improve customer interactions. However, as AI continues to revolutionise credit broking, staying ahead of these technological advances will be essential to maintaining a competitive edge. Interest and Investment Goldman Sachs forecasted in 2023 that annual global investments in AI technology would reach nearly $200 billion by 2025. Recent data suggests that investor enthusiasm for generative AI is accelerating even faster than anticipated. According to EY, venture capital investment in gen AI nearly doubled in 2024, reaching $45 billion -- up from $24 billion in 2023 and more than five times the $8.7 billion invested in 2022. Meanwhile, financial tracker PitchBook reports that generative AI companies secured a record-breaking $56 billion in venture capital across 885 deals in 2024. Source: https://www.goldmansachs.com/insights/articles/ai-investment-forecast-to-approach-200-billion-globally-by-2025 Beyond investment growth, the market itself is projected to expand significantly. Bloomberg Intelligence estimates that the generative AI sector could grow from $40 billion in 2022 to a staggering $1.3 trillion by 2032, with a compound annual growth rate (CAGR) of 42%. Source: https://www.bloomberg.com/company/press/generative-ai-to-become-a-1-3-trillion-market-by-2032-research-finds/ For the financial sector, particularly credit broking and credit risk management, this AI revolution is already underway. McKinsey's survey of senior credit risk executives from 24 financial institutions, including nine of the top ten US banks, found that 20% had already implemented at least one generative AI use case, while another 60% expected to do so within a year. AI-powered tools are being deployed across the credit life cycle, from hyper-personalised client engagement to automated credit assessments, underwriting, portfolio monitoring, and risk reporting. Portfolio monitoring, in particular, has become a key focus, with nearly 60% of institutions leveraging AI-driven optimisation strategies to enhance risk management and efficiency. Use Cases and Their Impacts Use cases for AI in credit broking are being revised, expanded and built upon all the while but there are a few essential ways in which its impact is already being felt. AI can be deployed, for example, to analyse and summarise unstructured data in ways that help speed up and enhance specific processes, handily saving businesses both time and money. Beyond efficiency, AI is transforming client engagement. By leveraging real-time data, AI-driven tools can assess individual financial situations with greater precision, offering hyper-personalised credit products. This is particularly impactful for those with limited or no credit history, as AI can analyse alternative data -- such as on transaction behaviour patterns, utility payments, and mobile usage -- to determine creditworthiness. As a result, AI is enabling more inclusive lending, helping individuals and businesses access financial products that would otherwise be out of reach. AI is also playing a crucial role in bridging the financial inclusion gap. AI-powered mobile banking and lending platforms are reaching underbanked populations by simplifying account setup, improving financial literacy, and providing tailored credit solutions. Advanced AI-driven chatbots and voice assistants are making financial services more accessible, particularly for those with limited literacy or technological experience. Additionally, AI-powered risk assessment tools are allowing micro-entrepreneurs and small businesses to secure funding, boosting economic growth in regions with limited traditional banking infrastructure. For credit brokers, generative AI can also mean more automation of routine processes. And once credit deals are approved, brokers should be able to streamline the contracting process with the help of AI. The tech can also, potentially at least, help brokers in putting together all and any written communications they need to send out to their clients, while information about those clients should also become richer and much easier to collect, assess and correlate. Challenges to Overcome For anyone involved in credit broking and risk assessment settings, there are clearly some major challenges to overcome as AI becomes an increasingly commonplace part of the picture. Crucially, as use of generative AI is scaled up, credit brokers need to take seriously a full range of issues associated with governance and risk. Regulators across financial services and worldwide are keeping a close eye on activities and developments around the use of AI, as they are bound to do in keeping with their remit as protectors of consumer interests and market integrity. As has always been the case for credit brokers, a fundamental aim must be to avoid any association with notions of unfairness. Standards in that respect will need to be maintained equally, or even improved upon, as generative AI comes into more widespread use and under the scrutiny of relevant regulators. The danger with letting standards slip in these contexts of course is that businesses might suffer significant reputational damage and trust in their services may wane substantively in ways that hinder their overall competitiveness. Transparency too is an important part of the equation for credit brokers making more common use of AI, with consumers and clients sure to expect that high standards of data privacy and security be maintained by any service providers they encounter or engage with. In simple terms, brokers ought to be able to confidently explain and justify, if ever asked, what they are doing with AI and why, whether they're responding to questions from clients, prospects, regulators, operating partners, or members of their own workforce. Best Laid Plans Taking a step back and looking at the broader picture around how credit brokers might aim to make best use of AI innovations in the coming years, planning ahead carefully rather than rushing to action could be key. There's no doubt that major credit risk players are embracing generative AI, but the challenges and risks involved also represent good reason for some degree of caution to be exercised as necessary. This somewhat cautious mindset is not limited to credit broking but extends across financial services and beyond. By mid-2024, IT decision-makers were increasingly grappling with the full scope of AI's implications. While optimism about AI's impact remains high, there is a growing focus on strategic planning, robust governance frameworks, data quality, employee upskilling, and scalability. A compelling example is Moody's, a leading credit ratings agency, which is modernising commercial lending with its new AI-powered solutions. By automating routine tasks such as loan origination and risk assessment, Moody's empowers staff to focus on strategic decisions while uncovering hidden insights through advanced data analysis. Although AI offers significant potential in automating various processes, the experiment by Clint Howen's Hero Broker highlighted that, in areas like mortgage broking, human interaction remains indispensable. Findings from the study revealed that 89.4% of borrowers preferred to speak to a real person before proceeding with their application, and only 1.4% completed the entire process online without any human help. In contrast to smaller financial products such as credit cards, which can be more easily managed through automated processes, home loans carry emotional weight that technology alone cannot manage. This emotional aspect of home ownership and borrowing makes the need for human support in such transactions critical. Ultimately, while AI is clearly poised to revolutionise many areas of the financial industry, a balanced approach, combining automation with human oversight, is key for the future of credit broking. AI enhances efficiency, but human expertise remains essential for managing the complexities of financial decisions. Credit brokers who blend both will be best positioned to succeed in the evolving financial landscape. Opportunities for Transformation Looking ahead, generative AI clearly has huge potential to transform credit industries worldwide, to boost financial inclusion, and to connect borrowers with lenders more seamlessly and efficiently than ever before. That potential is already compelling and, in years to come, AI will no doubt be used not just to address pain points or speed up specific processes, but throughout the credit broking life cycle in ways that are eventually taken completely for granted. Salesforce figures showed recently that younger cohorts of consumers, particularly those within the 'Gen Z' generation, are most ready for and happy to encounter gen AI services and solutions to better understand what to do with their data. Those findings tie in neatly with the notion that AI technology will inevitably become much more commonplace and widely relied upon in years to come in data-driven contexts like credit broking. For brokers themselves, there are risks to be considered carefully, as there are with any emerging and potentially game-changing technologies. The key to success may well be embracing the challenges that the AI revolution brings, while also trusting that demand for human expertise and experience that consistently makes a positive difference will always be in high demand.